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Reinforcement Learning: A Survey
TLDR
A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.Abstract:
This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.read more
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Journal Article
Hidden-mode Markov decision processes for nonstationary sequential decision making
TL;DR: The hidden-mode Markov decision process (HM-MDP) model as mentioned in this paper is a generalization of MDP that allows the model parameters to change probabilistically and is applicable to many traffic control type problems.
Journal ArticleDOI
Plasticity in Value Systems and its Role in Adaptive Behavior
TL;DR: It is suggested that plasticity in sensory afferents to value systems may provide a neurobiological basis for mediating the changing effects of saliency on adaptive behavioral responses.
Journal ArticleDOI
A new reinforcement learning vehicle control architecture for vision-based road following
TL;DR: The proposed three-stage image processing algorithm and the use of all six strips of edges have been capable of handling most of the uncertainties arising from the nonideal road conditions.
Journal ArticleDOI
Improved human-robot team performance through cross-training, an approach inspired by human team training practices
TL;DR: The hypothesis that the effective and fluent teaming of a human and a robot may best be achieved by modeling known, effective human teamwork practices is supported.
Proceedings ArticleDOI
Computing Optimal Stationary Policies for Multi-Objective Markov Decision Processes
Marco A. Wiering,E.D. de Jong +1 more
TL;DR: It is proved that the CON-MODP algorithm converges to the Pareto optimal set of value functions and policies for deterministic infinite horizon discounted multi-objective Markov decision processes.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Book
Markov Decision Processes: Discrete Stochastic Dynamic Programming
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
Book
Dynamic Programming and Optimal Control
TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
Book
Parallel and Distributed Computation: Numerical Methods
TL;DR: This work discusses parallel and distributed architectures, complexity measures, and communication and synchronization issues, and it presents both Jacobi and Gauss-Seidel iterations, which serve as algorithms of reference for many of the computational approaches addressed later.